CVE-2025-46059: LangChain GmailToolkit RCE Vulnerability

CVE-2025-46059 Overview

CVE-2025-46059 is an indirect prompt injection vulnerability discovered in langchain-ai version 0.3.51, specifically affecting the GmailToolkit component. This vulnerability allows attackers to execute arbitrary code and potentially compromise applications through crafted email messages processed by the vulnerable component.

It is important to note that this CVE is disputed by the supplier (LangChain). The vendor contends that the code-execution issue was introduced by user-written code that does not adhere to LangChain's documented security practices, rather than being an inherent flaw in the library itself.

Critical Impact

Attackers can achieve arbitrary code execution through malicious email content, potentially leading to complete application compromise and unauthorized system access.

Affected Products

• langchain-ai v0.3.51
• LangChain GmailToolkit component
• Applications using GmailToolkit without proper security controls

Discovery Timeline

• 2025-07-29 - CVE CVE-2025-46059 published to NVD
• 2025-08-04 - Last updated in NVD database

Technical Details for CVE-2025-46059

Vulnerability Analysis

This vulnerability is classified as CWE-94 (Improper Control of Generation of Code), which encompasses code injection attacks. The indirect prompt injection occurs within the GmailToolkit component, where malicious content embedded in email messages can manipulate the underlying language model to execute unintended code.

Indirect prompt injection attacks against LLM-integrated applications represent an emerging threat class where attackers exploit the trust boundary between user input and model-processed content. In this case, the attack surface is email messages that are processed by GmailToolkit, allowing external attackers to inject malicious prompts without direct access to the application.

The disputed nature of this CVE highlights the complex security model of AI agent frameworks, where responsibility may be shared between the framework provider and application developers implementing proper input validation and sandboxing.

Root Cause

The root cause stems from insufficient input sanitization and trust boundary violations when processing email content through the GmailToolkit component. When email messages containing malicious prompt injection payloads are processed, the content can manipulate the LLM agent into executing arbitrary code operations.

LangChain disputes this characterization, arguing that proper implementation following their security documentation would prevent such exploitation. This suggests the vulnerability may arise from implementation gaps in user-developed code rather than the core library.

Attack Vector

The attack leverages email as a delivery mechanism for indirect prompt injection. An attacker crafts a malicious email containing specially designed prompts that, when processed by an application using GmailToolkit, manipulate the LLM agent into executing arbitrary code. This network-based attack requires no authentication and no user interaction beyond the normal email processing workflow.

The attack chain typically involves:

1. Attacker sends a crafted email to a target monitored by the vulnerable application
2. GmailToolkit retrieves and processes the malicious email content
3. Embedded prompt injection payload manipulates the LLM agent
4. The compromised agent executes attacker-controlled code or commands

Technical details and proof-of-concept information are available in the GitHub CVE details repository.

Detection Methods for CVE-2025-46059

Indicators of Compromise

• Unusual LLM agent behavior or unexpected code execution during email processing
• Anomalous outbound connections or data exfiltration following email retrieval operations
• Log entries showing unexpected tool calls or command executions triggered by GmailToolkit
• Email content containing prompt injection patterns or suspicious instruction sequences

Detection Strategies

• Implement content analysis for emails processed by GmailToolkit to identify prompt injection patterns
• Monitor LLM agent execution logs for anomalous tool invocations or code execution requests
• Deploy application-level monitoring to detect unusual behavior following email processing
• Analyze email content for known prompt injection markers and suspicious instruction formats

Monitoring Recommendations

• Enable verbose logging for all GmailToolkit operations and LLM agent interactions
• Implement alerting for any code execution or system command invocations triggered by email processing
• Monitor network traffic for unexpected connections initiated after GmailToolkit email retrieval
• Review agent conversation logs for signs of prompt manipulation or injection attempts

How to Mitigate CVE-2025-46059

Immediate Actions Required

• Review all applications using langchain-ai v0.3.51 with GmailToolkit integration
• Audit existing implementations against LangChain security documentation
• Implement strict input validation and sanitization for all email content before LLM processing
• Consider disabling or restricting GmailToolkit functionality until proper security controls are in place

Patch Information

As this vulnerability is disputed by the vendor, no official patch has been released specifically addressing CVE-2025-46059. LangChain maintains that following their documented security practices prevents exploitation. Organizations should consult the LangChain security documentation for implementation guidance.

For additional context on the dispute and vendor response, see the GitHub community discussion and LangChain issue #30833.

Workarounds

• Implement content filtering and sanitization layers before email content reaches GmailToolkit
• Run LLM agent code in sandboxed environments with restricted permissions and capability controls
• Disable automatic code execution capabilities in LangChain agent configurations
• Apply principle of least privilege to all tools and capabilities exposed to the LLM agent
• Consider implementing human-in-the-loop verification for any code execution requests

# Configuration example - Restricting agent capabilities
# Disable dangerous tools in LangChain agent configuration
# Implement allowlisting for permitted agent operations
# Example: Set environment variable to disable code execution
export LANGCHAIN_DISABLE_CODE_EXECUTION=true

# Run application with restricted permissions
# Use containerization to limit blast radius
docker run --read-only --security-opt no-new-privileges your-langchain-app